1. Field of the Invention
The invention relates to digital image processing and viewing, particularly automatic suggesting or processing of enhancements of a digital image using information gained from identifying and analyzing regions within an image or features appearing within the image, particularly for creating post acquisition slide shows. The invention provides automated image analysis and processing methods and tools for photographs taken and/or images detected, acquired or captured in digital form or converted to digital form, or rendering from digital form to a soft or hard copy medium by using information about the regions or features in the photographs and/or images.
2. Description of the Related Art
This invention relates to finding and defining regions of interest (ROI) in an acquired image. In many cases the interest relates to items in the foreground of an image. In addition, and particularly for consumer photography, the ROI relates to human subjects and in particular, faces.
Although well-known, the problem of face detection has not received a great deal of attention from researchers. Most conventional techniques concentrate on face recognition, assuming that a region of an image containing a single face has already been extracted and will be provided as an input. Such techniques are usable to detect faces against complex backgrounds or when there are multiple occurrences in an image. For all of the image enhancement techniques introduced below and others as may be described herein or understood by those skilled in the art, it is desired to make use of the data obtained from face detection processes for suggesting options for improving digital images or for automatically improving or enhancing quality of digital images.
Yang et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pages 34-58, give a useful and comprehensive review of face detection techniques January 2002. These authors discuss various methods of face detection which may be divided into four main categories: (i) knowledge-based methods; (ii) feature-invariant approaches, including the identification of facial features, texture and skin color; (iii) template matching methods, both fixed and deformable and (iv) appearance based methods, included eigenface techniques, statistical distribution based methods and neural network approaches. They also discuss a number of the main applications for face detections technology. It is recognized in the present invention that none of this prior art describes or suggests using detection and knowledge of faces in images to create and/or use tools for the enhancement or correction of the images.
a. Faces as Subject Matter
Human faces may well be by far the most photographed subject matter for the amateur and professional photographer. In addition, the human visual system is very sensitive to faces in terms of skin tone colors. Also, its experiments performed by tracking the eye movement of the subjects, with an image that includes a human being, subjects tend to focus first and foremost on the face and in particular the eyes, and only later search the image around the figure. By default, when a picture includes a human figure and in particular a face, the face becomes the main object of the image. Thus, many artists and art teachers emphasize the location of the human figure and the face in particular to be an important part of a pleasing composition. For example, some teach to position faces around the “Golden Ratio”, also known as the “divine proportion” in the Renaissance period, or PHI, φ-lines. Some famous artists whose work repeatedly depict this composition are Leonardo Da-Vinci, Georges Seurat and Salvador Dali.
In addition, the faces themselves, not just the location of the faces in an image, have similar “divine proportion” characteristics. The head forms a golden rectangle with the eyes at its midpoint, the mouth and nose are each placed at golden sections of distance between the eyes and the bottom on the chin etc. etc.
b. Color and Exposure of Faces
While the human visual system is tolerant to shifts in color balance, the human skin tone is one area where the tolerance is somewhat limited and is accepted primarily only around the luminance axis, which is a main varying factor between skin tones of faces of people of different races or ethnic backgrounds. A knowledge of faces can provide an important advantage in methods of suggesting or automatically correcting an overall color balance of an image, as well as providing pleasing images after correction.
c. Auto Focus
Auto focusing is a popular feature among professional and amateur photographers alike. There are various ways to determine a region of focus. Some cameras use a center-weighted approach, while others allow the user to manually select the region. In most cases, it is the intention of the photographer to focus on the faces photographed, regardless of their location in the image. Other more sophisticated techniques include an attempt to guess the important regions of the image by determining the exact location where the photographer's, eye is looking. It is desired to provide advantageous auto focus techniques which can focus on what is considered the important subject in the image.
d. Fill-Flash
Another useful feature particularly for the amateur photographer is fill-flash mode. In this mode, objects close to the camera may receive a boost in their exposure using artificial light such as a flash, while far away objects which are not effected by the flash are exposed using available light. It is desired to have an advantageous technique which automatically provides image enhancements or suggested options using fill flash to add light to faces in the foreground which are in the shadow or shot with back light.
e. Orientation
The camera can be held horizontally or vertically when the picture is taken, creating what is referred to as a landscape mode or portrait mode, respectively. When viewing images, it is preferable to determine ahead of time the orientation of the camera at acquisition, thus eliminating a step of rotating the image and automatically orienting the image. The system may try to determine if the image was shot horizontally, which is also referred to as landscape format, where the width is larger than the height of an image, or vertically, also referred to as portrait mode, where the height of the image is larger than the width. Techniques may be used to determine as orientation of as image. Primarily these techniques include either recording the camera orientation at an acquisition time using an in camera mechanical indicator or attempting to analyze image content post-acquisition. In-camera methods, although providing precision, use additional hardware and sometimes movable hardware components which can increase the price of the camera and add a potential maintenance challenge. However, post-acquisition analysis may not generally provide sufficient precision. Knowledge of location, size and orientation of faces in a photograph, a computerized system can offer powerful automatic tools to enhance and correct such images or to provide options for enhancing and correcting images.
f. Color Correction
Automatic color correction can involve adding or removing a color cast to or from an image. Such cast can be created for many reasons including the film or CCD being calibrated to one light source, such as daylight, while the lighting condition at the time of image detection may be different, for example, cool-white fluorescent. In the example, an image can tend to have a greenish cast that it will be desired to be removed. It is desired to have automatically generated or suggested color correction techniques for use with digital image enhancement processing.
g. Cropping
Automatic cropping may be performed on an image to create a more pleasing compositions of an image. It is desired to have automatic image processing techniques for generating or suggesting more balanced image compositions using cropping.
h. Rendering
When an image is being rendered for printing or display, it undergoes operation as color conversion, contrast enhancement, cropping and/or resizing to accommodate the physical characteristics of the rendering device. Such characteristic may be a limited color gamut, a restricted aspect ratio, a restricted display orientation, fixed contrast ratio, etc. It is desired to have automatic image processing techniques for improving the rendering of images.
i. Compression and Resolution
An image can be locally compressed in accordance with a preferred embodiment herein, so that specific regions may have a higher quality compression which involves a lower compression rate. It is desired to have an advantageous technique for determining and/or selecting regions of importance that may be maintained with low compression or high resolution compared with regions determined and/or selected to have less importance in the image.